






"""simple docstring"""


import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import AutoencoderKL, PNDMScheduler, StableDiffusionInpaintPipeline, UNetaDConditionModel
from diffusers.utils import floats_tensor, load_image, load_numpy, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, slow

from ..pipeline_params import TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS, TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()




class 						__UpperCamelCase     (     UpperCamelCase   ,     UpperCamelCase   ,     UpperCamelCase   ,     unittest.TestCase						):
					lowercase_    :				List[Any]      =  StableDiffusionInpaintPipeline
					lowercase_    :				Union[str, Any]      =  TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
					lowercase_    :				Dict      =  TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
					lowercase_    :				Dict      =  frozenset(
					    []						)  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
					lowercase_    :				Optional[int]      =  frozenset([]						)
					def        UpperCAmelCase__ (							self							:	Tuple   )	->       Optional[int]:
											torch.manual_seed(0   )
											lowerCAmelCase   :List[Any]						=			UNetaDConditionModel(
											    block_out_channels=(32, 64)     , layers_per_block=2     , sample_size=32     , in_channels=9     , out_channels=4     , down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D')     , up_block_types=('CrossAttnUpBlock2D', 'UpBlock2D')     , cross_attention_dim=32     , attention_head_dim=(2, 4)     , use_linear_projection=UpperCAmelCase     , )
											lowerCAmelCase   :Optional[int]						=			PNDMScheduler(skip_prk_steps=UpperCAmelCase   )
											torch.manual_seed(0   )
											lowerCAmelCase   :str						=			AutoencoderKL(
											    block_out_channels=[32, 64]     , in_channels=3     , out_channels=3     , down_block_types=['DownEncoderBlock2D', 'DownEncoderBlock2D']     , up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D']     , latent_channels=4     , sample_size=128     , )
											torch.manual_seed(0   )
											lowerCAmelCase   :Optional[Any]						=			CLIPTextConfig(
											    bos_token_id=0     , eos_token_id=2     , hidden_size=32     , intermediate_size=37     , layer_norm_eps=1e-0_5     , num_attention_heads=4     , num_hidden_layers=5     , pad_token_id=1     , vocab_size=1000     , hidden_act='gelu'     , projection_dim=512     , )
											lowerCAmelCase   :Any						=			CLIPTextModel(UpperCAmelCase   )
											lowerCAmelCase   :List[str]						=			CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'   )

											lowerCAmelCase   :Optional[int]						=			{
											    'unet': unet,
											    'scheduler': scheduler,
											    'vae': vae,
											    'text_encoder': text_encoder,
											    'tokenizer': tokenizer,
											    'safety_checker': None,
											    'feature_extractor': None,
											}
											return components
					def        UpperCAmelCase__ (							self							:	List[Any]     , UpperCAmelCase							:	Dict     , UpperCAmelCase							:	int=0   )	->       List[str]:
											# TODO: use tensor inputs instead of PIL, this is here just to leave the old expected_slices untouched
											lowerCAmelCase   :Any						=			floats_tensor((1, 3, 32, 32)     , rng=random.Random(UpperCAmelCase   )   ).to(UpperCAmelCase   )
											lowerCAmelCase   :Optional[int]						=			image.cpu().permute(0     , 2     , 3     , 1   )[0]
											lowerCAmelCase   :int						=			Image.fromarray(np.uinta(UpperCAmelCase   )   ).convert('RGB'   ).resize((64, 64)   )
											lowerCAmelCase   :Any						=			Image.fromarray(np.uinta(image + 4   )   ).convert('RGB'   ).resize((64, 64)   )
											if str(UpperCAmelCase   ).startswith('mps'   ):
																	lowerCAmelCase   :Dict						=			torch.manual_seed(UpperCAmelCase   )
											else:
																	lowerCAmelCase   :Union[str, Any]						=			torch.Generator(device=UpperCAmelCase   ).manual_seed(UpperCAmelCase   )
											lowerCAmelCase   :str						=			{
											    'prompt': 'A painting of a squirrel eating a burger',
											    'image': init_image,
											    'mask_image': mask_image,
											    'generator': generator,
											    'num_inference_steps': 2,
											    'guidance_scale': 6.0,
											    'output_type': 'numpy',
											}
											return inputs
					def        UpperCAmelCase__ (							self							:	Dict   )	->       Optional[int]:
											lowerCAmelCase   :Optional[Any]						=			'cpu'  # ensure determinism for the device-dependent torch.Generator
											lowerCAmelCase   :Union[str, Any]						=			self.get_dummy_components()
											lowerCAmelCase   :str						=			StableDiffusionInpaintPipeline(**UpperCAmelCase   )
											lowerCAmelCase   :str						=			sd_pipe.to(UpperCAmelCase   )
											sd_pipe.set_progress_bar_config(disable=UpperCAmelCase   )

											lowerCAmelCase   :Dict						=			self.get_dummy_inputs(UpperCAmelCase   )
											lowerCAmelCase   :str						=			sd_pipe(**UpperCAmelCase   ).images
											lowerCAmelCase   :List[Any]						=			image[0, -3:, -3:, -1]

											assert image.shape == (1, 64, 64, 3)
											lowerCAmelCase   :Dict						=			np.array([0.4_7_2_7, 0.5_7_3_5, 0.3_9_4_1, 0.5_4_4_6, 0.5_9_2_6, 0.4_3_9_4, 0.5_0_6_2, 0.4_6_5_4, 0.4_4_7_6]   )

											assert np.abs(image_slice.flatten() - expected_slice   ).max() < 1e-2
					def        UpperCAmelCase__ (							self							:	List[Any]   )	->       List[str]:
											super().test_inference_batch_single_identical(expected_max_diff=3e-3   )




@slow
@require_torch_gpu
class 						__UpperCamelCase     (     unittest.TestCase						):
					def        UpperCAmelCase__ (							self							:	List[Any]   )	->       List[Any]:
											# clean up the VRAM after each test
											super().tearDown()
											gc.collect()
											torch.cuda.empty_cache()
					def        UpperCAmelCase__ (							self							:	Tuple   )	->       Optional[Any]:
											lowerCAmelCase   :Any						=			load_image(
											    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
											    '/sd2-inpaint/init_image.png'   )
											lowerCAmelCase   :Optional[Any]						=			load_image(
											    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png'   )
											lowerCAmelCase   :Union[str, Any]						=			load_numpy(
											    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint'
											    '/yellow_cat_sitting_on_a_park_bench.npy'   )

											lowerCAmelCase   :str						=			'stabilityai/stable-diffusion-2-inpainting'
											lowerCAmelCase   :List[str]						=			StableDiffusionInpaintPipeline.from_pretrained(UpperCAmelCase     , safety_checker=UpperCAmelCase   )
											pipe.to(UpperCAmelCase   )
											pipe.set_progress_bar_config(disable=UpperCAmelCase   )
											pipe.enable_attention_slicing()

											lowerCAmelCase   :Tuple						=			'Face of a yellow cat, high resolution, sitting on a park bench'

											lowerCAmelCase   :Tuple						=			torch.manual_seed(0   )
											lowerCAmelCase   :str						=			pipe(
											    prompt=UpperCAmelCase     , image=UpperCAmelCase     , mask_image=UpperCAmelCase     , generator=UpperCAmelCase     , output_type='np'     , )
											lowerCAmelCase   :Union[str, Any]						=			output.images[0]

											assert image.shape == (512, 512, 3)
											assert np.abs(expected_image - image   ).max() < 9e-3
					def        UpperCAmelCase__ (							self							:	str   )	->       List[str]:
											lowerCAmelCase   :Tuple						=			load_image(
											    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
											    '/sd2-inpaint/init_image.png'   )
											lowerCAmelCase   :Tuple						=			load_image(
											    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png'   )
											lowerCAmelCase   :int						=			load_numpy(
											    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint'
											    '/yellow_cat_sitting_on_a_park_bench_fp16.npy'   )

											lowerCAmelCase   :Tuple						=			'stabilityai/stable-diffusion-2-inpainting'
											lowerCAmelCase   :int						=			StableDiffusionInpaintPipeline.from_pretrained(
											    UpperCAmelCase     , torch_dtype=torch.floataa     , safety_checker=UpperCAmelCase     , )
											pipe.to(UpperCAmelCase   )
											pipe.set_progress_bar_config(disable=UpperCAmelCase   )
											pipe.enable_attention_slicing()

											lowerCAmelCase   :Dict						=			'Face of a yellow cat, high resolution, sitting on a park bench'

											lowerCAmelCase   :Any						=			torch.manual_seed(0   )
											lowerCAmelCase   :Dict						=			pipe(
											    prompt=UpperCAmelCase     , image=UpperCAmelCase     , mask_image=UpperCAmelCase     , generator=UpperCAmelCase     , output_type='np'     , )
											lowerCAmelCase   :List[Any]						=			output.images[0]

											assert image.shape == (512, 512, 3)
											assert np.abs(expected_image - image   ).max() < 5e-1
					def        UpperCAmelCase__ (							self							:	Optional[Any]   )	->       Any:
											torch.cuda.empty_cache()
											torch.cuda.reset_max_memory_allocated()
											torch.cuda.reset_peak_memory_stats()

											lowerCAmelCase   :Tuple						=			load_image(
											    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
											    '/sd2-inpaint/init_image.png'   )
											lowerCAmelCase   :int						=			load_image(
											    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png'   )

											lowerCAmelCase   :Tuple						=			'stabilityai/stable-diffusion-2-inpainting'
											lowerCAmelCase   :List[Any]						=			PNDMScheduler.from_pretrained(UpperCAmelCase     , subfolder='scheduler'   )
											lowerCAmelCase   :List[Any]						=			StableDiffusionInpaintPipeline.from_pretrained(
											    UpperCAmelCase     , safety_checker=UpperCAmelCase     , scheduler=UpperCAmelCase     , torch_dtype=torch.floataa     , )
											pipe.to(UpperCAmelCase   )
											pipe.set_progress_bar_config(disable=UpperCAmelCase   )
											pipe.enable_attention_slicing(1   )
											pipe.enable_sequential_cpu_offload()

											lowerCAmelCase   :Union[str, Any]						=			'Face of a yellow cat, high resolution, sitting on a park bench'

											lowerCAmelCase   :Optional[Any]						=			torch.manual_seed(0   )
											lowerCAmelCase   :Union[str, Any]						=			pipe(
											    prompt=UpperCAmelCase     , image=UpperCAmelCase     , mask_image=UpperCAmelCase     , generator=UpperCAmelCase     , num_inference_steps=2     , output_type='np'     , )

											lowerCAmelCase   :Optional[Any]						=			torch.cuda.max_memory_allocated()
											# make sure that less than 2.65 GB is allocated
											assert mem_bytes < 2.6_5 * 10**9







"""simple docstring"""


from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


__SCREAMING_SNAKE_CASE   =   {
    'configuration_transfo_xl': ['TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP', 'TransfoXLConfig'],
    'tokenization_transfo_xl': ['TransfoXLCorpus', 'TransfoXLTokenizer'],
}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__SCREAMING_SNAKE_CASE   =   [
		    'TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST',
		    'AdaptiveEmbedding',
		    'TransfoXLForSequenceClassification',
		    'TransfoXLLMHeadModel',
		    'TransfoXLModel',
		    'TransfoXLPreTrainedModel',
		    'load_tf_weights_in_transfo_xl',
		]

try:
		if not is_tf_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__SCREAMING_SNAKE_CASE   =   [
		    'TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST',
		    'TFAdaptiveEmbedding',
		    'TFTransfoXLForSequenceClassification',
		    'TFTransfoXLLMHeadModel',
		    'TFTransfoXLMainLayer',
		    'TFTransfoXLModel',
		    'TFTransfoXLPreTrainedModel',
		]


if TYPE_CHECKING:
		from .configuration_transfo_xl import TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP, TransfoXLConfig
		from .tokenization_transfo_xl import TransfoXLCorpus, TransfoXLTokenizer

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_transfo_xl import (
				    TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST,
				    AdaptiveEmbedding,
				    TransfoXLForSequenceClassification,
				    TransfoXLLMHeadModel,
				    TransfoXLModel,
				    TransfoXLPreTrainedModel,
				    load_tf_weights_in_transfo_xl,
				)

		try:
				if not is_tf_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_tf_transfo_xl import (
				    TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST,
				    TFAdaptiveEmbedding,
				    TFTransfoXLForSequenceClassification,
				    TFTransfoXLLMHeadModel,
				    TFTransfoXLMainLayer,
				    TFTransfoXLModel,
				    TFTransfoXLPreTrainedModel,
				)

else:
		import sys

		__SCREAMING_SNAKE_CASE   =   _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)